OpenRaiser/Intern-Atlas huggingface dataset:
Efficient and Practical Approximation Algorithms for Advertising in Content Feeds
Venue: arXiv cs.DS 2025
Year: 2025

Content feeds provided by platforms such as X (formerly Twitter) and TikTok are consumed by users on a daily basis. In this paper, we revisit the native advertising problem in content feeds, initiated by Ieong et al. Given a sequence of organic items (e.g., videos or posts) relevant to a user's interests or to an information search, the goal is to place ads within the organic content so as to maximize a reward function (e.g., number of clicks), while accounting for two considerations: (1) an ad can only be inserted after a relevant content item; (2) the users' attention decays after consuming content or ads. These considerations provide a natural model for capturing both the advertisement effectiveness and the user experience. In this paper, we design fast and practical 2-approximation greedy algorithms for the associated optimization problem, improving over the best-known practical algorithm that only achieves an approximation factor of~4. Our algorithms exploit a counter-intuitive observation, namely, while top items are seemingly more important due to the decaying attention of the user, taking good care of the bottom items is key for obtaining improved approximation guarantees. We then provide the first comprehensive empirical evaluation on the problem, showing the strong empirical performance of our~methods.
Standard Neural Computation Alone Is Insufficient for Logical Intelligence
Venue: arXiv cs.AI 2025
Year: 2025

Neural networks, as currently designed, fall short of achieving true logical intelligence. Modern AI models rely on standard neural computation-inner-product-based transformations and nonlinear activations-to approximate patterns from data. While effective for inductive learning, this architecture lacks the structural guarantees necessary for deductive inference and logical consistency. As a result, deep networks struggle with rule-based reasoning, structured generalization, and interpretability without extensive post-hoc modifications. This position paper argues that standard neural layers must be fundamentally rethought to integrate logical reasoning. We advocate for Logical Neural Units (LNUs)-modular components that embed differentiable approximations of logical operations (e.g., AND, OR, NOT) directly within neural architectures. We critique existing neurosymbolic approaches, highlight the limitations of standard neural computation for logical inference, and present LNUs as a necessary paradigm shift in AI. Finally, we outline a roadmap for implementation, discussing theoretical foundations, architectural integration, and key challenges for future research.
DOC-Depth: A novel approach for dense depth ground truth generation
Venue: arXiv cs.CV 2025
Year: 2025

Accurate depth information is essential for many computer vision applications. Yet, no available dataset recording method allows for fully dense accurate depth estimation in a large scale dynamic environment. In this paper, we introduce DOC-Depth, a novel, efficient and easy-to-deploy approach for dense depth generation from any LiDAR sensor. After reconstructing consistent dense 3D environment using LiDAR odometry, we address dynamic objects occlusions automatically thanks to DOC, our state-of-the art dynamic object classification method. Additionally, DOC-Depth is fast and scalable, allowing for the creation of unbounded datasets in terms of size and time. We demonstrate the effectiveness of our approach on the KITTI dataset, improving its density from 16.1% to 71.2% and release this new fully dense depth annotation, to facilitate future research in the domain. We also showcase results using various LiDAR sensors and in multiple environments. All software components are publicly available for the research community.
Generative Kernel Spectral Clustering
Venue: arXiv cs.LG 2025
Year: 2025

Modern clustering approaches often trade interpretability for performance, particularly in deep learning-based methods. We present Generative Kernel Spectral Clustering (GenKSC), a novel model combining kernel spectral clustering with generative modeling to produce both well-defined clusters and interpretable representations. By augmenting weighted variance maximization with reconstruction and clustering losses, our model creates an explorable latent space where cluster characteristics can be visualized through traversals along cluster directions. Results on MNIST and FashionMNIST datasets demonstrate the model's ability to learn meaningful cluster representations.
Extending the Applicability of Bloom Filters by Relaxing their Parameter Constraints
Venue: arXiv cs.DS 2025
Year: 2025

These days, Key-Value Stores are widely used for scalable data storage. In this environment, Bloom filter (BF) serves as an efficient probabilistic data structure for representing sets of keys. They allow for set membership queries with no false negatives and with the right choice of the main parameters - length of the BF, number of hash functions used to map an element to the array's indices, and the number of elements inserted - the false positive rate is optimized. However, the number of hash functions is constrained to integer values, and the length of a BF is usually chosen to be a power of two to allow for efficient modulo operations using binary arithmetic. In this paper, we relax these constraints by proposing the Rational Bloom filter, which allows for non-integer numbers of hash functions. This results in optimized fraction-of-zero values for a known number of elements to be inserted. Based on this, we construct the Variably-Sized Block BF to allow for a flexible filter length, especially for large filters, with efficient computation.
When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown remarkable success in language modelling due to scaling laws found in model size and the hidden dimension of the model's text representation. Yet, we demonstrate that compressed representations of text can yield better performance in LLM-based regression tasks. In this paper, we compare the relative performance of embedding compression in three different signal-to-noise contexts: financial return prediction, writing quality assessment and review scoring. Our results show that compressing embeddings, in a minimally supervised manner using an autoencoder's hidden representation, can mitigate overfitting and improve performance on noisy tasks, such as financial return prediction; but that compression reduces performance on tasks that have high causal dependencies between the input and target data. Our results suggest that the success of interpretable compressed representations such as sentiment may be due to a regularising effect.
Digital Fairness Algorithms for Satellite Uplink NOMA
Venue: arXiv cs.IT 2025
Year: 2025

Achieving digital fairness by using NOMA is one of the more pressing issues in modern wireless communication systems for 5G/6G networks. This is particularly true in the case of satellite uplink systems supporting a population of IoT wireless devices scattered in a wide coverage area. In this scenario, the variability of the link budget across space and time increases the challenges of preventing a situation where only a subset of network users can transmit while others are left unable to do so. This work investigates the characteristics of an uplink NOMA system with the goal of equalizing the achievable rate of the IoT network subscribers. Within the context of single-slot NOMA, two key outcomes are achieved: the determination of the optimal SIC ordering at the receiver and the exploration of power moderation, coordinated by the receiver, to maximize the minimum user rate. In the context of multi-slot NOMA, which is particularly relevant to the satellite scenario under consideration, a user rate equalization algorithm is proposed and its performance is analyzed numerically. The trade-off between network performance, measured in terms of user rates, and complexity, determined by the number of SIC steps implemented at the receiver, is thoroughly evaluated for the satellite scenario under consideration.
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control
Venue: arXiv cs.LG 2025
Year: 2025

High-precision control tasks present substantial challenges for reinforcement learning (RL) algorithms, frequently resulting in suboptimal performance attributed to network approximation inaccuracies and inadequate sample quality.These issues are exacerbated when the task requires the agent to achieve a precise goal state, as is common in robotics and other real-world applications.We introduce Adviser-Actor-Critic (AAC), designed to address the precision control dilemma by combining the precision of feedback control theory with the adaptive learning capability of RL and featuring an Adviser that mentors the actor to refine control actions, thereby enhancing the precision of goal attainment.Finally, through benchmark tests, AAC outperformed standard RL algorithms in precision-critical, goal-conditioned tasks, demonstrating AAC's high precision, reliability, and robustness.Code are available at: https://anonymous.4open.science/r/Adviser-Actor-Critic-8AC5.
Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
Venue: arXiv cs.LG 2025
Year: 2025

In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas. This imbalance presents significant challenges for existing classification methods with clear class boundaries, while highlighting a scarcity of approaches specifically designed for imbalanced regression problems. To better address these issues, we introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density. Furthermore, we propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness. Through several experiments, EDS has shown its effectiveness, and the related code and dataset can be accessed at https://anonymous.4open.science/r/Error-Distribution-Smoothing-762F.
Target Attack Backdoor Malware Analysis and Attribution
Venue: arXiv cs.CR 2025
Year: 2025

Backdoor Malware are installed by an attacker on the victim's server(s) for authorized access. A customized backdoor is weaponized to execute unauthorized system, database and application commands to access the user credentials and confidential digital assets. Recently, we discovered and analyzed a targeted persistent module backdoor in Web Server in an online business company that was undetectable by their deployed Anti-Virus software for a year. This led us to carry out research to detect this specific type of persistent module backdoor installed in Web servers. Other than typical Malware static analysis, we carry out analysis with binary similarity, strings, and command obfuscation over the backdoor, resulting in the Target Attack Backdoor Malware Analysis Matrix (TABMAX) for organizations to detect this sophisticated target attack backdoor instead of a general one which can be detected by Anti-Virus detectors. Our findings show that backdoor malware can be designed with different APIs, commands, strings, and query language on top of preferred libraries used by typical Malware.
A Minimax Approach to Ad Hoc Teamwork
Venue: arXiv cs.AI 2025
Year: 2025

We propose a minimax-Bayes approach to Ad Hoc Teamwork (AHT) that optimizes policies against an adversarial prior over partners, explicitly accounting for uncertainty about partners at time of deployment. Unlike existing methods that assume a specific distribution over partners, our approach improves worst-case performance guarantees. Extensive experiments, including evaluations on coordinated cooking tasks from the Melting Pot suite, show our method's superior robustness compared to self-play, fictitious play, and best response learning. Our work highlights the importance of selecting an appropriate training distribution over teammates to achieve robustness in AHT.
CVKAN: Complex-Valued Kolmogorov-Arnold Networks
Venue: arXiv cs.LG 2025
Year: 2025

In this work we propose CVKAN, a complex-valued Kolmogorov-Arnold Network (KAN), to join the intrinsic interpretability of KANs and the advantages of Complex-Valued Neural Networks (CVNNs). We show how to transfer a KAN and the necessary associated mechanisms into the complex domain. To confirm that CVKAN meets expectations we conduct experiments on symbolic complex-valued function fitting and physically meaningful formulae as well as on a more realistic dataset from knot theory. Our proposed CVKAN is more stable and performs on par or better than real-valued KANs while requiring less parameters and a shallower network architecture, making it more explainable.
The Algebraic Cost of a Boolean Sum
Venue: arXiv cs.CC 2025
Year: 2025

It is a well-known fact that the permanent polynomial is complete for the complexity class VNP, and it is largely suspected that the determinant does not share this property, despite its similar expression. We study the question of why the VNP-completeness proof of the permanent fails for the determinant. We isolate three fundamental properties that are sufficient to prove a polynomial sequence is VNP-hard, of which two are shared by both the permanent and the determinant. We proceed to show that the permanent satisfies the third property, which we refer to as the ``cost of a boolean sum," while the determinant does not, showcasing the fundamental difference between the polynomial families. We further note that this differentiation also applies in the border complexity setting and that our results apply for counting complexity.
A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions
Venue: arXiv cs.RO 2025
Year: 2025

In recent years, the focus on developing robot manipulators has shifted towards prioritizing safety in Human-Robot Interaction (HRI). Impedance control is a typical approach for interaction control in collaboration tasks. However, such a control approach has two main limitations: 1) the end-effector (EE)'s limited compliance to adapt to unknown physical interactions, and 2) inability of the robot body to compliantly adapt to unknown physical interactions. In this work, we present an approach to address these drawbacks. We introduce a modified Cartesian impedance control method combined with a Dynamical System (DS)-based motion generator, aimed at enhancing the interaction capability of the EE without compromising main task tracking performance. This approach enables human coworkers to interact with the EE on-the-fly, e.g. tool changeover, after which the robot compliantly resumes its task. Additionally, combining with a new null space impedance control method enables the robot body to exhibit compliant behaviour in response to interactions, avoiding serious injuries from accidental contact while mitigating the impact on main task tracking performance. Finally, we prove the passivity of the system and validate the proposed approach through comprehensive comparative experiments on a 7 Degree-of-Freedom (DOF) KUKA LWR IV+ robot.
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Venue: arXiv cs.CV 2025
Year: 2025

We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA pairs, 2,300 object captioning, and 5,700 object grounding annotations, encompassing diverse real-world conditions such as adverse weather and traffic anomalies. By incorporating tuple-based spatio-temporal object expressions, TUMTraffic-VideoQA unifies three essential tasks-multiple-choice video question answering, referred object captioning, and spatio-temporal object grounding-within a cohesive evaluation framework. We further introduce the TUMTraffic-Qwen baseline model, enhanced with visual token sampling strategies, providing valuable insights into the challenges of fine-grained spatio-temporal reasoning. Extensive experiments demonstrate the dataset's complexity, highlight the limitations of existing models, and position TUMTraffic-VideoQA as a robust foundation for advancing research in intelligent transportation systems. The dataset and benchmark are publicly available to facilitate further exploration.
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users and object instances, and to generate contextually tailored responses. Existing approaches typically rely on time-consuming test-time training for each user or object, making them impractical for real-world deployment, a limitation reflected in current personalization benchmarks, which are focused on object-centric, single-concept evaluations. In this paper, we present a novel training-free approach to LVLM personalization and introduce a comprehensive real-world benchmark designed to rigorously evaluate various aspects of the personalization task. Our method leverages pre-trained vision foundation models to extract distinctive features, applies retrieval-augmented generation (RAG) techniques to identify instances within visual inputs, and employs visual prompting strategies to guide model outputs. Our model-agnostic vision toolkit enables efficient and flexible multi-concept personalization across both images and videos, without any additional training. We achieve state-of-the-art results, surpassing existing training-based methods.
A note on Ordered Ruzsa-Szemerédi graphs
Venue: arXiv cs.DS 2025
Year: 2025

A recent breakthrough of Behnezhad and Ghafari [FOCS 2024] and subsequent work of Assadi, Khanna, and Kiss [SODA 2025] gave algorithms for the fully dynamic $(1-\varepsilon)$-approximate maximum matching problem whose runtimes are determined by a purely combinatorial quantity: the maximum density of Ordered Ruzsa-Szemerédi (ORS) graphs. We say a graph $G$ is an $(r,t)$-ORS graph if its edges can be partitioned into $t$ matchings $M_1,M_2, \ldots, M_t$ each of size $r$, such that for every $i$, $M_i$ is an induced matching in the subgraph $M_{i} \cup M_{i+1} \cup \cdots \cup M_t$. This is a relaxation of the extensively-studied notion of a Ruzsa-Szemerédi (RS) graph, the difference being that in an RS graph each $M_i$ must be an induced matching in $G$. In this note, we show that these two notions are roughly equivalent. Specifically, let $\mathrm{ORS}(n)$ be the largest $t$ such that there exists an $n$-vertex ORS-$(Ω(n), t)$ graph, and define $\mathrm{RS}(n)$ analogously. We show that if $\mathrm{ORS}(n) \ge Ω(n^c)$, then for any fixed $δ> 0$, $\mathrm{RS}(n) \ge Ω(n^{c(1-δ)})$. This resolves a question of Behnezhad and Ghafari.
Towards Consistent and Controllable Image Synthesis for Face Editing
Venue: arXiv cs.CV 2025
Year: 2025

Face editing methods, essential for tasks like virtual avatars, digital human synthesis and identity preservation, have traditionally been built upon GAN-based techniques, while recent focus has shifted to diffusion-based models due to their success in image reconstruction. However, diffusion models still face challenges in controlling specific attributes and preserving the consistency of other unchanged attributes especially the identity characteristics. To address these issues and facilitate more convenient editing of face images, we propose a novel approach that leverages the power of Stable-Diffusion (SD) models and crude 3D face models to control the lighting, facial expression and head pose of a portrait photo. We observe that this task essentially involves the combinations of target background, identity and face attributes aimed to edit. We strive to sufficiently disentangle the control of these factors to enable consistency of face editing. Specifically, our method, coined as RigFace, contains: 1) A Spatial Attribute Encoder that provides presise and decoupled conditions of background, pose, expression and lighting; 2) A high-consistency FaceFusion method that transfers identity features from the Identity Encoder to the denoising UNet of a pre-trained SD model; 3) An Attribute Rigger that injects those conditions into the denoising UNet. Our model achieves comparable or even superior performance in both identity preservation and photorealism compared to existing face editing models.
Optimal Security Response to Network Intrusions in IT Systems
Venue: arXiv cs.GT 2025
Year: 2025

Cybersecurity is one of the most pressing technological challenges of our time and requires measures from all sectors of society. A key measure is automated security response, which enables automated mitigation and recovery from cyber attacks. Significant strides toward such automation have been made due to the development of rule-based response systems. However, these systems have a critical drawback: they depend on domain experts to configure the rules, a process that is both error-prone and inefficient. Framing security response as an optimal control problem shows promise in addressing this limitation but introduces new challenges. Chief among them is bridging the gap between theoretical optimality and operational performance. Current response systems with theoretical optimality guarantees have only been validated analytically or in simulation, leaving their practical utility unproven. This thesis tackles the aforementioned challenges by developing a practical methodology for optimal security response in IT infrastructures. It encompasses two systems. First, it includes an emulation system that replicates key components of the target infrastructure. We use this system to gather measurements and logs, based on which we identify a game-theoretic model. Second, it includes a simulation system where game-theoretic response strategies are optimized through stochastic approximation to meet a given objective, such as mitigating potential attacks while maintaining operational services. These strategies are then evaluated and refined in the emulation system to close the gap between theoretical and operational performance. We prove structural properties of optimal response strategies and derive efficient algorithms for computing them. This enables us to solve a previously unsolved problem: demonstrating optimal security response against network intrusions on an IT infrastructure.
A Family-Based Approach to Safety Cases for Controlled Airspaces in Small Uncrewed Aerial Systems
Venue: arXiv cs.SE 2025
Year: 2025

As small Uncrewed Aircraft Systems (sUAS) increasingly operate in the national airspace, safety concerns arise due to a corresponding rise in reported airspace violations and incidents, highlighting the need for a safe mechanism for sUAS entry control to manage the potential overload. This paper presents work toward our aim of establishing automated, customized safety-claim support for managing on-entry requests from sUAS to enter controlled airspace. We describe our approach, Safety Case Software Product Line Engineering (SafeSPLE), which is a novel method to extend product-family techniques to on-entry safety cases. It begins with a hazard analysis and design of a safety case feature model defining key points in variation, followed by the creation of a parameterized safety case. We use these together to automate the generation of instances for specific sUAS. Finally we use a case study to demonstrate that the SafeSPLE method can be used to facilitate creation of safety cases for specific flights.